Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604621
Title: Non-linear model predictive control strategies for process plants using soft computing approaches
Author: Owa, Kayode Olayemi
ISNI:       0000 0004 5357 2870
Awarding Body: University of Plymouth
Current Institution: University of Plymouth
Date of Award: 2014
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Abstract:
The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systems.
Supervisor: Sharma, Sanjay Sponsor: Petroleum Training Development Fund
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.604621  DOI: Not available
Keywords: Model Predictive Control ; Wavelet Neural Network ; Genetic Algorithm ; Particle Swarm Optimisation ; Real time Optimisation ; Coupled Tank System ; Quadruple Tank Process ; Soft Computing Techniques ; Non-linear Model ; Multi-variable Systems
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